Prof. Gallier, who’s a Professor in the Computer and Information Science Department, School of Engineering and Applied Science at the University of Pennsylvania, launches a new book Algebra, Topology, Differential Calculus, and Optimization Theory For Computer Science and Machine Learning.That’s almost 2,000 pages of math,“math basics” is quite the misnomer—it gives the impression that one would need to study all of the contents of this book to be an effective practitioner in CS or ML. Memorizing every definition and theorem in this book would be neither necessary nor sufficient for that purpose.Keep in mind it can take an hour, and sometimes way more, to really absorb a single page of a math book like this (do the math). This is more of a reference text.but Great book.Keep in mind it can take an hour, and sometimes way more, to really absorb a single page of a math book like this (do the math). This is more of a reference text.

I think it’s a good time to mention a couple of nice books (related).

  1. Mathematics For Machine Learning by Deisentoth, Faisal, Ong Elementary intro to math of machine learning . Its style is a bit less austere than that of Prof’s. It also has a chapter on probability. It could possible serve as a great prequel to the Prof’s book.

  2. Foundations Of Data Science By Blum, Hopcroft, Kannan The book on probability related topics of general data science: high-dimensional geometry, random walks, Markov chains, random graphs, various related algorithms etc [1]

  3. Pure Mathematics for Beginners: A Rigorous Introduction to Logic, Set Theory, Abstract Algebra, Number Theory, Real Analysis, Topology, Complex Analysis, and Linear Algebra by Steve Warner Support for people who’d like to read books like the one, but never seen any kind of higher math before . This book has a cover that screams trashy book extremely skimpy on actual info (anyone who reads a lot of tech books knows what I am talking about), but surprisingly,it contains everything it says it does and in great detail. Not even actual math textbooks (say, Springer) are usually written with this much detail. Author likes to add bullet point style elaboration to almost every definition and theorem which is (almost) never the case with gazillions of books usually titled “Abstract Algebra”, “Real Analysis”, “Complex Analysis” etc. Some such books sometimes attach words like “friendly” to their title (say, “Friendly Measure Theory For Idiots”) and still do not rise to the occasion. Worse yet, a ton (if not most) of these books are exact clones of each other with different author names attached. The linked book doesn’t suffer from any of these problems.